Under the hood, Python’s multiprocessing package spins up a new python process for each core of the processor. Thus, to speed up our Python script we can utilize multiprocessing. We are only using 5% of our true processing power! The Python script will then run to completion. The operating system will assign the process to a single one of those cores: Figure 2: Without multiprocessing, your OpenCV program may not be efficiently using all cores or processors available on your machine. Now, let’s assume we launch our Python script. This figure is meant to visualize the 3 GHz Intel Xeon W on my iMac Pro - note how the processor has a total of 20 cores. This 3GHz Intel Xeon W processor is being underutilized. By default, Python scripts use a single process. To see how we’re underutilizing our processor, consider the following image: Figure 1: Multiprocessing with OpenCV and Python. That’s all fine and good - but we are only utilizing a small amount of our true processing power. The OS then allows the Python script to run on the processor core until completion.The operating system assigns the Python program to a single core of the processor.via the OS however, for the sake of simplicity, let’s assume: There are entire books dedicated to multiprocessing, operating systems, and how processes are scheduled, assigned, removed, deleted, etc. How the actual Python process itself is assigned to a CPU core is dependent on how the operating system handles (1) process scheduling and (2) assigning system vs. When you launch your Python project, the python binary launches a Python interpreter (i.e., the “Python process”). The vast majority of projects and applications you have implemented are (very likely) single-threaded. Let’s get started! Why use multiprocessing for processing a dataset of images? And distributing the load across all cores of the processor.I’ll also discuss why immediately jumping to Big Data algorithms, tools, and paradigms (such as Hadoop and MapReduce) is the wrong decision - instead, you should parallelize across the system bus first.įrom there we’ll implement our Python and OpenCV multiprocessing functions to facilitate processing a large dataset quickly and easily.įinally, we’ll put all the pieces together and compare how long it takes to process our dataset: multi-threaded applications, including why we may choose to use multiprocessing with OpenCV to speed up the processing of a given dataset. In the first part of this tutorial, we’ll discuss single-threaded vs. Looking for the source code to this post? Jump Right To The Downloads Section Multiprocessing with OpenCV and Python To learn how to use multiprocessing with OpenCV and Python, just keep reading. I’ll show you in the rest of this tutorial. The image hashing post she is referring to is singled threaded, meaning that only one core of the processor is being utilized - if we switch to using multiple threads/processes we can dramatically speed up the hashing process.īut how do we actually utilize multiprocessing with OpenCV and Python? Is there anything I can do to speedup the process? I used your code to perform image hashing but it’s taking a long time to process the entire dataset. They have provided me with a dataset of ~7.5 million images. I’m trying to apply image hashing to my research project at the university. Hey Adrian, I just read your tutorial on image hashing with OpenCV and really enjoyed it. Today’s tutorial is inspired by PyImageSearch reader, Abigail. You’ll learn how to use multiprocessing with OpenCV to parallelize feature extraction across the system bus, including all processors and cores on your computer. In this tutorial, you will learn how to use multiprocessing with OpenCV and Python to perform feature extraction. Click here to download the source code to this post
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